Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study

摘要

Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive, and not routinely available in most centers. We present a tissue non-destructive method for automated BCR prognosis, termed "Histotyping", that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Patients from two institutions (n?=?214) were used to train Histotyping for identifying high-risk patients based on six features of glandular morphology extracted from RP specimens. Histotyping was validated for post-RP BCR prognosis on a separate set of n?=?675 patients from five institutions and compared against Decipher on n?=?167 patients. Histotyping was prognostic of BCR in the validation set (p??0.001, univariable hazard ratio [HR]?=?2.83, 95% confidence interval [CI]: 2.03–3.93, concordance index [c-index]?=?0.68, median years-to-BCR: 1.7). Histotyping was also prognostic in clinically stratified subsets, such as patients with Gleason grade group 3 (HR?=?4.09) and negative surgical margins (HR?=?3.26). Histotyping was prognostic independent of grade group, margin status, pathological stage, and preoperative prostate-specific antigen (PSA) (multivariable p??0.001, HR?=?2.09, 95% CI: 1.40–3.10, n?=?648). The combination of Histotyping, grade group, and preoperative PSA outperformed Decipher (c-index?=?0.75 vs. 0.70, n?=?167). These results suggest that a prognostic classifier for prostate cancer based on digital images could serve as an alternative or complement to molecular-based companion diagnostic tests.